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id="post-info"><h1 class="post-title">强化学习入门 Demo</h1><div id="post-meta"><div class="meta-firstline"><span class="post-meta-date"><i class="far fa-calendar-alt fa-fw post-meta-icon"></i><span class="post-meta-label">发表于</span><time class="post-meta-date-created" datetime="2020-04-27T13:15:24.000Z" title="发表于 2020-04-27 21:15:24">2020-04-27</time><span class="post-meta-separator">|</span><i class="fas fa-history fa-fw post-meta-icon"></i><span class="post-meta-label">更新于</span><time class="post-meta-date-updated" datetime="2021-08-14T04:39:34.295Z" title="更新于 2021-08-14 12:39:34">2021-08-14</time></span><span class="post-meta-categories"><span class="post-meta-separator">|</span><i class="fas fa-inbox fa-fw post-meta-icon"></i><a class="post-meta-categories" href="/categories/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/">机器学习</a><i class="fas fa-angle-right post-meta-separator"></i><i class="fas fa-inbox fa-fw post-meta-icon"></i><a class="post-meta-categories" 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id="article-container"><h1 id="Q-Learning"><a href="#Q-Learning" class="headerlink" title="Q Learning"></a>Q Learning</h1><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br><span class="line">31</span><br><span class="line">32</span><br><span class="line">33</span><br><span class="line">34</span><br><span class="line">35</span><br><span class="line">36</span><br><span class="line">37</span><br><span class="line">38</span><br><span class="line">39</span><br><span class="line">40</span><br><span class="line">41</span><br><span class="line">42</span><br><span class="line">43</span><br><span class="line">44</span><br><span class="line">45</span><br><span class="line">46</span><br><span class="line">47</span><br><span class="line">48</span><br><span class="line">49</span><br><span class="line">50</span><br><span class="line">51</span><br><span class="line">52</span><br><span class="line">53</span><br><span class="line">54</span><br><span class="line">55</span><br><span class="line">56</span><br><span class="line">57</span><br><span class="line">58</span><br><span class="line">59</span><br><span class="line">60</span><br><span class="line">61</span><br><span class="line">62</span><br><span class="line">63</span><br><span class="line">64</span><br><span class="line">65</span><br><span class="line">66</span><br><span class="line">67</span><br><span class="line">68</span><br><span class="line">69</span><br><span class="line">70</span><br><span class="line">71</span><br><span class="line">72</span><br><span class="line">73</span><br><span class="line">74</span><br><span class="line">75</span><br><span class="line">76</span><br><span class="line">77</span><br><span class="line">78</span><br><span class="line">79</span><br><span class="line">80</span><br><span class="line">81</span><br><span class="line">82</span><br><span class="line">83</span><br><span class="line">84</span><br><span class="line">85</span><br><span class="line">86</span><br><span class="line">87</span><br><span class="line">88</span><br><span class="line">89</span><br><span class="line">90</span><br><span class="line">91</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">import</span> numpy <span class="keyword">as</span> np</span><br><span class="line"><span class="keyword">import</span> pandas <span class="keyword">as</span> pd</span><br><span class="line"><span class="keyword">import</span> time</span><br><span class="line"><span class="keyword">import</span> IPython</span><br><span class="line"></span><br><span class="line"></span><br><span class="line">length = <span class="number">4</span>  <span class="comment"># 道路长度</span></span><br><span class="line">epsilon = <span class="number">0.9</span>  <span class="comment"># 贪心值</span></span><br><span class="line">a = [<span class="number">0</span>, <span class="number">1</span>]  <span class="comment"># 动作</span></span><br><span class="line">Q_a = np.zeros([length + <span class="number">1</span>, <span class="built_in">len</span>(a)])  <span class="comment"># Q-a表</span></span><br><span class="line">alpha = <span class="number">0.1</span></span><br><span class="line">gamma = <span class="number">0.9</span></span><br><span class="line">game_over = <span class="literal">False</span>  <span class="comment"># 一轮游戏是否结束</span></span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">print_environment</span>(<span class="params">state</span>):</span></span><br><span class="line">    <span class="string">&quot;&quot;&quot;打印环境&quot;&quot;&quot;</span></span><br><span class="line">    <span class="built_in">str</span> = <span class="string">&#x27;-&#x27;</span> * state</span><br><span class="line">    <span class="built_in">str</span> += <span class="string">&#x27;o&#x27;</span></span><br><span class="line">    <span class="built_in">str</span> += <span class="string">&#x27;-&#x27;</span> * (length - state - <span class="number">1</span>)</span><br><span class="line">    <span class="keyword">if</span> state != length:</span><br><span class="line">        <span class="built_in">str</span> += <span class="string">&#x27;$&#x27;</span></span><br><span class="line">    print(<span class="built_in">str</span>)</span><br><span class="line"></span><br><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">best_Q</span>(<span class="params">state</span>):</span></span><br><span class="line">    <span class="string">&quot;&quot;&quot;最佳动作获得的Q&quot;&quot;&quot;</span></span><br><span class="line">    <span class="keyword">return</span> Q_a[state].<span class="built_in">max</span>()</span><br><span class="line"></span><br><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">best_action</span>(<span class="params">state</span>):</span></span><br><span class="line">    <span class="string">&quot;&quot;&quot;最佳动作, 有多个最大从中随机选择一个&quot;&quot;&quot;</span></span><br><span class="line">    max_arr = np.argsort(Q_a[state])[::-<span class="number">1</span>]  <span class="comment"># 排序</span></span><br><span class="line">    max_index = np.random.randint(<span class="number">0</span>, np.<span class="built_in">sum</span>(Q_a[state] == Q_a[state][max_arr[<span class="number">0</span>]]))  <span class="comment"># 随机选择</span></span><br><span class="line">    <span class="keyword">return</span> max_arr[max_index]</span><br><span class="line"></span><br><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">update_Q</span>(<span class="params">state, next_state, action, reward</span>):</span></span><br><span class="line">    <span class="string">&quot;&quot;&quot;更新Q表&quot;&quot;&quot;</span></span><br><span class="line">    Q_a[state][action] += alpha * (reward + gamma * best_Q(next_state) - Q_a[state][action])</span><br><span class="line"></span><br><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">greedy</span>(<span class="params">state</span>):</span></span><br><span class="line">    <span class="string">&quot;&quot;&quot;贪婪策略&quot;&quot;&quot;</span></span><br><span class="line">    <span class="keyword">if</span> np.random.rand() &lt; epsilon:</span><br><span class="line">        <span class="keyword">return</span> best_action(state)</span><br><span class="line">    <span class="keyword">else</span>:</span><br><span class="line">        <span class="keyword">return</span> np.random.randint(<span class="number">0</span>, Q_a[state].shape[<span class="number">0</span>])  <span class="comment"># 随机动作</span></span><br><span class="line"></span><br><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">update_state</span>(<span class="params">now_state, action</span>):</span></span><br><span class="line">    <span class="string">&quot;&quot;&quot;更新环境及获取奖赏&quot;&quot;&quot;</span></span><br><span class="line">    <span class="keyword">if</span> action == <span class="number">0</span>:</span><br><span class="line">        <span class="keyword">if</span> now_state != <span class="number">0</span>:</span><br><span class="line">            next_state = now_state - <span class="number">1</span></span><br><span class="line">            reward = <span class="number">0</span></span><br><span class="line">        <span class="keyword">else</span>:</span><br><span class="line">            next_state = now_state</span><br><span class="line">            reward = <span class="number">0</span></span><br><span class="line">    <span class="keyword">else</span>:</span><br><span class="line">        <span class="keyword">if</span> now_state == length - <span class="number">1</span>:</span><br><span class="line">            next_state = now_state + <span class="number">1</span></span><br><span class="line">            reward = <span class="number">1</span>  <span class="comment"># 到达终点奖励</span></span><br><span class="line">            <span class="keyword">global</span> game_over</span><br><span class="line">            game_over = <span class="literal">True</span>  <span class="comment"># 游戏结束</span></span><br><span class="line">        <span class="keyword">else</span>:</span><br><span class="line">            next_state = now_state + <span class="number">1</span></span><br><span class="line">            reward = <span class="number">0</span></span><br><span class="line">    print_environment(next_state)</span><br><span class="line">    <span class="keyword">return</span> next_state, reward</span><br><span class="line"></span><br><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">Q_learning</span>(<span class="params">start, rounds, max_times</span>):</span></span><br><span class="line">    <span class="string">&quot;&quot;&quot;start是起点, rounds是轮数, max_times是每轮迭代最大次数&quot;&quot;&quot;</span></span><br><span class="line">    state = start</span><br><span class="line">    <span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(rounds):</span><br><span class="line">        state = start</span><br><span class="line">        print_environment(state)</span><br><span class="line">        time.sleep(<span class="number">1</span>)</span><br><span class="line">        IPython.display.clear_output()  <span class="comment"># 清空输出</span></span><br><span class="line">        <span class="keyword">for</span> j <span class="keyword">in</span> <span class="built_in">range</span>(max_times):</span><br><span class="line">            <span class="keyword">global</span> game_over</span><br><span class="line">            <span class="keyword">if</span> game_over:</span><br><span class="line">                game_over = <span class="literal">False</span></span><br><span class="line">                print(<span class="string">&#x27;Game Over&#x27;</span>)</span><br><span class="line">                time.sleep(<span class="number">1</span>)</span><br><span class="line">                IPython.display.clear_output()  <span class="comment"># 清空输出</span></span><br><span class="line">                <span class="keyword">break</span></span><br><span class="line">            action = greedy(state)</span><br><span class="line">            next_state, reward = update_state(state, action)</span><br><span class="line">            update_Q(state, next_state, action, reward)</span><br><span class="line">            state = next_state</span><br><span class="line">            time.sleep(<span class="number">1</span>)</span><br><span class="line">            IPython.display.clear_output()  <span class="comment"># 清空输出</span></span><br><span class="line"></span><br><span class="line">            </span><br><span class="line">Q_learning(<span class="number">0</span>, <span class="number">5</span>, <span class="number">100</span>)</span><br></pre></td></tr></table></figure>
<h1 id="Sarsa"><a href="#Sarsa" class="headerlink" title="Sarsa"></a>Sarsa</h1><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br><span class="line">31</span><br><span class="line">32</span><br><span class="line">33</span><br><span class="line">34</span><br><span class="line">35</span><br><span class="line">36</span><br><span class="line">37</span><br><span class="line">38</span><br><span class="line">39</span><br><span class="line">40</span><br><span class="line">41</span><br><span class="line">42</span><br><span class="line">43</span><br><span class="line">44</span><br><span class="line">45</span><br><span class="line">46</span><br><span class="line">47</span><br><span class="line">48</span><br><span class="line">49</span><br><span class="line">50</span><br><span class="line">51</span><br><span class="line">52</span><br><span class="line">53</span><br><span class="line">54</span><br><span class="line">55</span><br><span class="line">56</span><br><span class="line">57</span><br><span class="line">58</span><br><span class="line">59</span><br><span class="line">60</span><br><span class="line">61</span><br><span class="line">62</span><br><span class="line">63</span><br><span class="line">64</span><br><span class="line">65</span><br><span class="line">66</span><br><span class="line">67</span><br><span class="line">68</span><br><span class="line">69</span><br><span class="line">70</span><br><span class="line">71</span><br><span class="line">72</span><br><span class="line">73</span><br><span class="line">74</span><br><span class="line">75</span><br><span class="line">76</span><br><span class="line">77</span><br><span class="line">78</span><br><span class="line">79</span><br><span class="line">80</span><br><span class="line">81</span><br><span class="line">82</span><br><span class="line">83</span><br><span class="line">84</span><br><span class="line">85</span><br><span class="line">86</span><br><span class="line">87</span><br><span class="line">88</span><br><span class="line">89</span><br><span class="line">90</span><br><span class="line">91</span><br><span class="line">92</span><br><span class="line">93</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">import</span> numpy <span class="keyword">as</span> np</span><br><span class="line"><span class="keyword">import</span> pandas <span class="keyword">as</span> pd</span><br><span class="line"><span class="keyword">import</span> time</span><br><span class="line"><span class="keyword">import</span> IPython</span><br><span class="line"></span><br><span class="line"></span><br><span class="line">length = <span class="number">5</span>  <span class="comment"># 道路长度(由于saras算法过于保守, 所以如果设太长的话...你可能得等半天)</span></span><br><span class="line">epsilon = <span class="number">0.9</span>  <span class="comment"># 贪心值</span></span><br><span class="line">a = [<span class="number">0</span>, <span class="number">1</span>]  <span class="comment"># 动作</span></span><br><span class="line">Q_a = np.zeros([length + <span class="number">1</span>, <span class="built_in">len</span>(a)])  <span class="comment"># Q-a表</span></span><br><span class="line">alpha = <span class="number">0.1</span></span><br><span class="line">gamma = <span class="number">0.9</span></span><br><span class="line">game_over = <span class="literal">False</span>  <span class="comment"># 一轮游戏是否结束</span></span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">print_environment</span>(<span class="params">state</span>):</span></span><br><span class="line">    <span class="string">&quot;&quot;&quot;打印环境&quot;&quot;&quot;</span></span><br><span class="line">    <span class="built_in">str</span> = <span class="string">&#x27;-&#x27;</span> * state</span><br><span class="line">    <span class="built_in">str</span> += <span class="string">&#x27;o&#x27;</span></span><br><span class="line">    <span class="built_in">str</span> += <span class="string">&#x27;-&#x27;</span> * (length - state - <span class="number">1</span>)</span><br><span class="line">    <span class="keyword">if</span> state != length:</span><br><span class="line">        <span class="built_in">str</span> += <span class="string">&#x27;$&#x27;</span></span><br><span class="line">    print(<span class="built_in">str</span>)</span><br><span class="line"></span><br><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">best_Q</span>(<span class="params">state</span>):</span></span><br><span class="line">    <span class="string">&quot;&quot;&quot;最佳动作获得的Q&quot;&quot;&quot;</span></span><br><span class="line">    <span class="keyword">return</span> Q_a[state].<span class="built_in">max</span>()</span><br><span class="line"></span><br><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">best_action</span>(<span class="params">state</span>):</span></span><br><span class="line">    <span class="string">&quot;&quot;&quot;最佳动作, 有多个最大从中随机选择一个&quot;&quot;&quot;</span></span><br><span class="line">    max_arr = np.argsort(Q_a[state])[::-<span class="number">1</span>]  <span class="comment"># 排序</span></span><br><span class="line">    max_index = np.random.randint(<span class="number">0</span>, np.<span class="built_in">sum</span>(Q_a[state] == Q_a[state][max_arr[<span class="number">0</span>]]))  <span class="comment"># 随机选择</span></span><br><span class="line">    <span class="keyword">return</span> max_arr[max_index]</span><br><span class="line"></span><br><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">update_Q</span>(<span class="params">state, next_state, action, next_action, reward</span>):</span></span><br><span class="line">    <span class="string">&quot;&quot;&quot;更新Q表, 相比Q Learning的差别就在于并非选取最佳动作获得的Q而是实际动作获得的Q&quot;&quot;&quot;</span></span><br><span class="line">    Q_a[state][action] += alpha * (reward + gamma * Q_a[next_state][next_action] - Q_a[state][action])</span><br><span class="line"></span><br><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">greedy</span>(<span class="params">state</span>):</span></span><br><span class="line">    <span class="string">&quot;&quot;&quot;贪婪策略&quot;&quot;&quot;</span></span><br><span class="line">    <span class="keyword">if</span> np.random.rand() &lt; epsilon:</span><br><span class="line">        <span class="keyword">return</span> best_action(state)</span><br><span class="line">    <span class="keyword">else</span>:</span><br><span class="line">        <span class="keyword">return</span> np.random.randint(<span class="number">0</span>, Q_a[state].shape[<span class="number">0</span>])  <span class="comment"># 随机动作</span></span><br><span class="line"></span><br><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">update_state</span>(<span class="params">now_state, action</span>):</span></span><br><span class="line">    <span class="string">&quot;&quot;&quot;更新环境及获取奖赏&quot;&quot;&quot;</span></span><br><span class="line">    <span class="keyword">if</span> action == <span class="number">0</span>:</span><br><span class="line">        <span class="keyword">if</span> now_state != <span class="number">0</span>:</span><br><span class="line">            next_state = now_state - <span class="number">1</span></span><br><span class="line">            reward = <span class="number">0</span></span><br><span class="line">        <span class="keyword">else</span>:</span><br><span class="line">            next_state = now_state</span><br><span class="line">            reward = <span class="number">0</span></span><br><span class="line">    <span class="keyword">else</span>:</span><br><span class="line">        <span class="keyword">if</span> now_state == length - <span class="number">1</span>:</span><br><span class="line">            next_state = now_state + <span class="number">1</span></span><br><span class="line">            reward = <span class="number">1</span>  <span class="comment"># 到达终点奖励</span></span><br><span class="line">            <span class="keyword">global</span> game_over</span><br><span class="line">            game_over = <span class="literal">True</span>  <span class="comment"># 游戏结束</span></span><br><span class="line">        <span class="keyword">else</span>:</span><br><span class="line">            next_state = now_state + <span class="number">1</span></span><br><span class="line">            reward = <span class="number">0</span></span><br><span class="line">    print_environment(next_state)</span><br><span class="line">    <span class="keyword">return</span> next_state, reward</span><br><span class="line"></span><br><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">Sarsa</span>(<span class="params">start, rounds, max_times</span>):</span></span><br><span class="line">    <span class="string">&quot;&quot;&quot;start是起点, rounds是轮数, max_times是每轮迭代最大次数&quot;&quot;&quot;</span></span><br><span class="line">    state = start</span><br><span class="line">    <span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(rounds):</span><br><span class="line">        state = start</span><br><span class="line">        print_environment(state)</span><br><span class="line">        time.sleep(<span class="number">1</span>)</span><br><span class="line">        IPython.display.clear_output()  <span class="comment"># 清空输出</span></span><br><span class="line">        action = greedy(state)</span><br><span class="line">        <span class="keyword">for</span> j <span class="keyword">in</span> <span class="built_in">range</span>(max_times):</span><br><span class="line">            <span class="keyword">global</span> game_over</span><br><span class="line">            <span class="keyword">if</span> game_over:</span><br><span class="line">                game_over = <span class="literal">False</span></span><br><span class="line">                print(<span class="string">&#x27;Game Over&#x27;</span>)</span><br><span class="line">                time.sleep(<span class="number">1</span>)</span><br><span class="line">                IPython.display.clear_output()  <span class="comment"># 清空输出</span></span><br><span class="line">                <span class="keyword">break</span></span><br><span class="line">            next_action = greedy(state)</span><br><span class="line">            next_state, reward = update_state(state, action)</span><br><span class="line">            update_Q(state, next_state, action, next_action, reward)</span><br><span class="line">            state = next_state</span><br><span class="line">            action = next_action</span><br><span class="line">            time.sleep(<span class="number">1</span>)</span><br><span class="line">            IPython.display.clear_output()  <span class="comment"># 清空输出</span></span><br><span class="line"></span><br><span class="line">            </span><br><span class="line">Sarsa(<span class="number">0</span>, <span class="number">10</span>, <span class="number">100</span>)</span><br></pre></td></tr></table></figure>

<h1 id="Sarsa-Lambda"><a href="#Sarsa-Lambda" class="headerlink" title="Sarsa Lambda"></a>Sarsa Lambda</h1><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br><span class="line">31</span><br><span class="line">32</span><br><span class="line">33</span><br><span class="line">34</span><br><span class="line">35</span><br><span class="line">36</span><br><span class="line">37</span><br><span class="line">38</span><br><span class="line">39</span><br><span class="line">40</span><br><span class="line">41</span><br><span class="line">42</span><br><span class="line">43</span><br><span class="line">44</span><br><span class="line">45</span><br><span class="line">46</span><br><span class="line">47</span><br><span class="line">48</span><br><span class="line">49</span><br><span class="line">50</span><br><span class="line">51</span><br><span class="line">52</span><br><span class="line">53</span><br><span class="line">54</span><br><span class="line">55</span><br><span class="line">56</span><br><span class="line">57</span><br><span class="line">58</span><br><span class="line">59</span><br><span class="line">60</span><br><span class="line">61</span><br><span class="line">62</span><br><span class="line">63</span><br><span class="line">64</span><br><span class="line">65</span><br><span class="line">66</span><br><span class="line">67</span><br><span class="line">68</span><br><span class="line">69</span><br><span class="line">70</span><br><span class="line">71</span><br><span class="line">72</span><br><span class="line">73</span><br><span class="line">74</span><br><span class="line">75</span><br><span class="line">76</span><br><span class="line">77</span><br><span class="line">78</span><br><span class="line">79</span><br><span class="line">80</span><br><span class="line">81</span><br><span class="line">82</span><br><span class="line">83</span><br><span class="line">84</span><br><span class="line">85</span><br><span class="line">86</span><br><span class="line">87</span><br><span class="line">88</span><br><span class="line">89</span><br><span class="line">90</span><br><span class="line">91</span><br><span class="line">92</span><br><span class="line">93</span><br><span class="line">94</span><br><span class="line">95</span><br><span class="line">96</span><br><span class="line">97</span><br><span class="line">98</span><br><span class="line">99</span><br><span class="line">100</span><br><span class="line">101</span><br><span class="line">102</span><br><span class="line">103</span><br><span class="line">104</span><br><span class="line">105</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">import</span> numpy <span class="keyword">as</span> np</span><br><span class="line"><span class="keyword">import</span> pandas <span class="keyword">as</span> pd</span><br><span class="line"><span class="keyword">import</span> time</span><br><span class="line"><span class="keyword">import</span> IPython</span><br><span class="line"></span><br><span class="line"></span><br><span class="line">length = <span class="number">5</span>  <span class="comment"># 道路长度</span></span><br><span class="line">epsilon = <span class="number">0.9</span>  <span class="comment"># 贪心值</span></span><br><span class="line">a = [<span class="number">0</span>, <span class="number">1</span>]  <span class="comment"># 动作</span></span><br><span class="line">Q_a = np.zeros([length + <span class="number">1</span>, <span class="built_in">len</span>(a)])  <span class="comment"># Q-a表</span></span><br><span class="line">E = Q_a.copy()  <span class="comment"># 状态表, 可以理解为遗忘程度 (0 即为完全遗忘, 1 即为完全记得, 按照遗忘程度来给出贡献)</span></span><br><span class="line">alpha = <span class="number">0.1</span></span><br><span class="line">gamma = <span class="number">0.9</span></span><br><span class="line">trace_decay = <span class="number">0.9</span>  <span class="comment"># lambda值</span></span><br><span class="line">game_over = <span class="literal">False</span>  <span class="comment"># 一轮游戏是否结束</span></span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">print_environment</span>(<span class="params">state</span>):</span></span><br><span class="line">    <span class="string">&quot;&quot;&quot;打印环境&quot;&quot;&quot;</span></span><br><span class="line">    <span class="built_in">str</span> = <span class="string">&#x27;-&#x27;</span> * state</span><br><span class="line">    <span class="built_in">str</span> += <span class="string">&#x27;o&#x27;</span></span><br><span class="line">    <span class="built_in">str</span> += <span class="string">&#x27;-&#x27;</span> * (length - state - <span class="number">1</span>)</span><br><span class="line">    <span class="keyword">if</span> state != length:</span><br><span class="line">        <span class="built_in">str</span> += <span class="string">&#x27;$&#x27;</span></span><br><span class="line">    print(<span class="built_in">str</span>)</span><br><span class="line"></span><br><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">best_Q</span>(<span class="params">state</span>):</span></span><br><span class="line">    <span class="string">&quot;&quot;&quot;最佳动作获得的Q&quot;&quot;&quot;</span></span><br><span class="line">    <span class="keyword">return</span> Q_a[state].<span class="built_in">max</span>()</span><br><span class="line"></span><br><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">best_action</span>(<span class="params">state</span>):</span></span><br><span class="line">    <span class="string">&quot;&quot;&quot;最佳动作, 有多个最大从中随机选择一个&quot;&quot;&quot;</span></span><br><span class="line">    max_arr = np.argsort(Q_a[state])[::-<span class="number">1</span>]  <span class="comment"># 排序</span></span><br><span class="line">    max_index = np.random.randint(<span class="number">0</span>, np.<span class="built_in">sum</span>(Q_a[state] == Q_a[state][max_arr[<span class="number">0</span>]]))  <span class="comment"># 随机选择</span></span><br><span class="line">    <span class="keyword">return</span> max_arr[max_index]</span><br><span class="line"></span><br><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">update_Q</span>(<span class="params">state, next_state, action, next_action, reward</span>):</span></span><br><span class="line">    <span class="string">&quot;&quot;&quot;更新Q表, 相比Sarsa的差别就在于E表起到了对之前的Q表更新的作用&quot;&quot;&quot;</span></span><br><span class="line">    <span class="comment"># 这里有两种更新方式, 可以都尝试一下</span></span><br><span class="line">    <span class="string">&quot;&quot;&quot;</span></span><br><span class="line"><span class="string">    E[state][action] += 1</span></span><br><span class="line"><span class="string">    &quot;&quot;&quot;</span></span><br><span class="line">    <span class="keyword">global</span> E</span><br><span class="line">    <span class="keyword">global</span> Q_a</span><br><span class="line">    E[state,:] = <span class="number">0</span></span><br><span class="line">    E[state][action] = <span class="number">1</span></span><br><span class="line">    Q_a += alpha * E * (reward + gamma * Q_a[next_state][next_action] - Q_a[state][action])</span><br><span class="line">    E *= trace_decay * gamma</span><br><span class="line"></span><br><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">greedy</span>(<span class="params">state</span>):</span></span><br><span class="line">    <span class="string">&quot;&quot;&quot;贪婪策略&quot;&quot;&quot;</span></span><br><span class="line">    <span class="keyword">if</span> np.random.rand() &lt; epsilon:</span><br><span class="line">        <span class="keyword">return</span> best_action(state)</span><br><span class="line">    <span class="keyword">else</span>:</span><br><span class="line">        <span class="keyword">return</span> np.random.randint(<span class="number">0</span>, Q_a[state].shape[<span class="number">0</span>])  <span class="comment"># 随机动作</span></span><br><span class="line"></span><br><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">update_state</span>(<span class="params">now_state, action</span>):</span></span><br><span class="line">    <span class="string">&quot;&quot;&quot;更新环境及获取奖赏&quot;&quot;&quot;</span></span><br><span class="line">    <span class="keyword">if</span> action == <span class="number">0</span>:</span><br><span class="line">        <span class="keyword">if</span> now_state != <span class="number">0</span>:</span><br><span class="line">            next_state = now_state - <span class="number">1</span></span><br><span class="line">            reward = <span class="number">0</span></span><br><span class="line">        <span class="keyword">else</span>:</span><br><span class="line">            next_state = now_state</span><br><span class="line">            reward = <span class="number">0</span></span><br><span class="line">    <span class="keyword">else</span>:</span><br><span class="line">        <span class="keyword">if</span> now_state == length - <span class="number">1</span>:</span><br><span class="line">            next_state = now_state + <span class="number">1</span></span><br><span class="line">            reward = <span class="number">1</span>  <span class="comment"># 到达终点奖励</span></span><br><span class="line">            <span class="keyword">global</span> game_over</span><br><span class="line">            game_over = <span class="literal">True</span>  <span class="comment"># 游戏结束</span></span><br><span class="line">        <span class="keyword">else</span>:</span><br><span class="line">            next_state = now_state + <span class="number">1</span></span><br><span class="line">            reward = <span class="number">0</span></span><br><span class="line">    print_environment(next_state)</span><br><span class="line">    <span class="keyword">return</span> next_state, reward</span><br><span class="line"></span><br><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">Sarsa_Lambda</span>(<span class="params">start, rounds, max_times</span>):</span></span><br><span class="line">    <span class="string">&quot;&quot;&quot;start是起点, rounds是轮数, max_times是每轮迭代最大次数&quot;&quot;&quot;</span></span><br><span class="line">    state = start</span><br><span class="line">    <span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(rounds):</span><br><span class="line">        state = start</span><br><span class="line">        print_environment(state)</span><br><span class="line">        time.sleep(<span class="number">1</span>)</span><br><span class="line">        IPython.display.clear_output()  <span class="comment"># 清空输出</span></span><br><span class="line">        action = greedy(state)</span><br><span class="line">        <span class="keyword">for</span> j <span class="keyword">in</span> <span class="built_in">range</span>(max_times):</span><br><span class="line">            <span class="keyword">global</span> game_over</span><br><span class="line">            <span class="keyword">if</span> game_over:</span><br><span class="line">                game_over = <span class="literal">False</span></span><br><span class="line">                print(<span class="string">&#x27;Game Over&#x27;</span>)</span><br><span class="line">                time.sleep(<span class="number">1</span>)</span><br><span class="line">                IPython.display.clear_output()  <span class="comment"># 清空输出</span></span><br><span class="line">                E[:] = <span class="number">0</span>  <span class="comment"># E表归0</span></span><br><span class="line">                <span class="keyword">break</span></span><br><span class="line">            next_action = greedy(state)</span><br><span class="line">            next_state, reward = update_state(state, action)</span><br><span class="line">            update_Q(state, next_state, action, next_action, reward)</span><br><span class="line">            state = next_state</span><br><span class="line">            action = next_action</span><br><span class="line">            time.sleep(<span class="number">1</span>)</span><br><span class="line">            IPython.display.clear_output()  <span class="comment"># 清空输出</span></span><br><span class="line"></span><br><span class="line">            </span><br><span class="line">Sarsa_Lambda(<span class="number">0</span>, <span class="number">10</span>, <span class="number">100</span>)</span><br></pre></td></tr></table></figure>



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